ordo-jit-optimization — engine ordo-jit-optimization, community, engine, ide skills, high-performance, rule-engine, rules-engine, Claude Code, Cursor, Windsurf

v1.0.0

このスキルについて

高性能エージェントが高度なJITコンパイルとスキーマ認識による直接メモリアクセスを必要とする場合に適しています。 Ordo JIT compilation and performance optimization guide. Includes Schema-aware JIT, TypedContext derive macro, Cranelift compilation, performance tuning. Use for optimizing rule execution performance, reducing latency, increasing throughput.

# Core Topics

Pama-Lee Pama-Lee
[24]
[2]
Updated: 3/10/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 7/11

This page remains useful for operators, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution
Review Score
7/11
Quality Score
44
Canonical Locale
en
Detected Body Locale
en

高性能エージェントが高度なJITコンパイルとスキーマ認識による直接メモリアクセスを必要とする場合に適しています。 Ordo JIT compilation and performance optimization guide. Includes Schema-aware JIT, TypedContext derive macro, Cranelift compilation, performance tuning. Use for optimizing rule execution performance, reducing latency, increasing throughput.

このスキルを使用する理由

CraneliftベースのJITコンパイルを使用して、スキーマ認識による直接メモリアクセスとExpr ASTを利用し、20-30倍のパフォーマンス改善を実現し、Rustベースの開発と金融アプリケーションをサポートします。

おすすめ

高性能エージェントが高度なJITコンパイルとスキーマ認識による直接メモリアクセスを必要とする場合に適しています。

実現可能なユースケース for ordo-jit-optimization

JITコンパイルを使用してルールエンジンのパフォーマンスを最適化する
スキーマ認識による直接メモリアクセスを使用して高性能金融モデルを生成する
Expr ASTをデバッグして実行効率を向上させる

! セキュリティと制限

  • Rustプログラミング言語が必要
  • CraneliftベースのJITコンパイルに依存している
  • スキーマ認識による直接メモリアクセスが最適なパフォーマンスを実現するために必要

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.
  • - The underlying skill quality score is below the review floor.

Source Boundary

The section below is supporting source material from the upstream repository. Use the Killer-Skills review above as the primary decision layer.

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FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is ordo-jit-optimization?

高性能エージェントが高度なJITコンパイルとスキーマ認識による直接メモリアクセスを必要とする場合に適しています。 Ordo JIT compilation and performance optimization guide. Includes Schema-aware JIT, TypedContext derive macro, Cranelift compilation, performance tuning. Use for optimizing rule execution performance, reducing latency, increasing throughput.

How do I install ordo-jit-optimization?

Run the command: npx killer-skills add Pama-Lee/Ordo/ordo-jit-optimization. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for ordo-jit-optimization?

Key use cases include: JITコンパイルを使用してルールエンジンのパフォーマンスを最適化する, スキーマ認識による直接メモリアクセスを使用して高性能金融モデルを生成する, Expr ASTをデバッグして実行効率を向上させる.

Which IDEs are compatible with ordo-jit-optimization?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for ordo-jit-optimization?

Rustプログラミング言語が必要. CraneliftベースのJITコンパイルに依存している. スキーマ認識による直接メモリアクセスが最適なパフォーマンスを実現するために必要.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add Pama-Lee/Ordo/ordo-jit-optimization. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use ordo-jit-optimization immediately in the current project.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

Imported Repository Instructions

The section below is supporting source material from the upstream repository. Use the Killer-Skills review above as the primary decision layer.

Supporting Evidence

ordo-jit-optimization

Install ordo-jit-optimization, an AI agent skill for AI agent workflows and automation. Works with Claude Code, Cursor, and Windsurf with one-command setup.

SKILL.md
Readonly
Imported Repository Instructions
The section below is supporting source material from the upstream repository. Use the Killer-Skills review above as the primary decision layer.
Supporting Evidence

Ordo JIT Compilation and Performance Optimization

Schema-Aware JIT

Ordo's JIT compiler is based on Cranelift, supporting Schema-aware direct memory access with 20-30x performance improvement.

Core Architecture

                    ┌─────────────────┐
                    │   Expr AST      │
                    └────────┬────────┘
                             │
                    ┌────────▼────────┐
                    │ SchemaJITCompiler│
                    └────────┬────────┘
                             │
              ┌──────────────┼──────────────┐
              │              │              │
     ┌────────▼────────┐    │    ┌────────▼────────┐
     │  Field Offset   │    │    │  Native Code    │
     │   Resolution    │    │    │   Generation    │
     └─────────────────┘    │    └─────────────────┘
                    ┌───────▼────────┐
                    │ Machine Code   │
                    │ ldr d0, [ptr+N]│
                    └────────────────┘

TypedContext Derive Macro

rust
1use ordo_derive::TypedContext; 2 3#[derive(TypedContext)] 4struct UserContext { 5 age: i64, 6 balance: f64, 7 vip_level: i64, 8 #[typed_context(skip)] // Skip non-numeric fields 9 name: String, 10}

The generated Schema contains field offsets, JIT compiler directly generates memory load instructions.

Using JIT Evaluator

rust
1use ordo_core::expr::jit::{SchemaJITCompiler, SchemaJITEvaluator}; 2 3// Create compiler 4let mut compiler = SchemaJITCompiler::new()?; 5 6// Compile expression (with Schema) 7let schema = UserContext::schema(); 8let compiled = compiler.compile_with_schema(&expr, &schema)?; 9 10// Execute 11let ctx = UserContext { age: 25, balance: 1000.0, vip_level: 3 }; 12let result = unsafe { compiled.call_typed(&ctx)? };

Performance Comparison

MethodLatencyUse Case
Interpreter~1.63 µsDynamic rules, development/debugging
Bytecode VM~200 nsGeneral purpose
Schema JIT~50-80 nsHigh-frequency execution, fixed Schema

Optimization Strategies

1. Expression Pre-compilation

rust
1// Pre-compile expressions when loading rules 2let mut ruleset = RuleSet::from_json(json)?; 3ruleset.compile()?; // Pre-compile all expressions to bytecode 4 5// Or use one-step loading 6let ruleset = RuleSet::from_json_compiled(json)?;

2. Batch Execution

rust
1use ordo_core::prelude::*; 2 3let executor = RuleExecutor::new(); 4 5// Batch execution (reduces lock contention) 6let inputs: Vec<Value> = load_batch(); 7let results = executor.execute_batch(&ruleset, inputs)?;

3. Vectorized Evaluation

rust
1use ordo_core::expr::VectorizedEvaluator; 2 3let evaluator = VectorizedEvaluator::new(); 4let contexts: Vec<Context> = prepare_contexts(); 5let results = evaluator.eval_batch(&expr, &contexts)?;

4. Function Fast Path

Common functions (len, sum, max, min, abs, count, is_null) have inline fast paths, avoiding HashMap lookups.

Compiler Configuration

JIT Compiler Options

rust
1let mut compiler = SchemaJITCompiler::new()?; 2 3// View compilation statistics 4let stats = compiler.stats(); 5println!("Successful compiles: {}", stats.successful_compiles); 6println!("Total code size: {} bytes", stats.total_code_size);

Feature Flags

Configure in Cargo.toml:

toml
1[dependencies] 2ordo-core = { version = "0.2", features = ["jit"] } 3 4# Full features 5ordo-core = { version = "0.2", features = ["default"] } # jit + signature + derive

Note: JIT is not available on WASM targets (Cranelift doesn't support wasm32).

Performance Tuning Checklist

Compile-time Optimization

  • Build with --release mode
  • Enable LTO: lto = true
  • Set codegen-units = 1

Runtime Optimization

  • Pre-compile rule expressions
  • Use JIT for fixed Schema
  • Batch execution to reduce overhead
  • Set reasonable max_depth

Server Optimization

  • Set RUST_LOG=warn or info
  • Disable unnecessary tracing
  • Use connection pooling
  • Configure appropriate worker count

Benchmarking

Run built-in benchmarks:

bash
1# Basic benchmarks 2cargo bench --package ordo-core 3 4# JIT comparison tests 5cargo bench --package ordo-core --bench jit_comparison_bench 6 7# Schema JIT tests 8cargo bench --package ordo-core --bench schema_jit_bench

Typical Results (Apple Silicon)

expression/eval/simple_compare    time: [79.234 ns]
expression/eval/function_call     time: [211.45 ns]
rule/simple_ruleset              time: [1.6312 µs]
jit/schema_aware/numeric         time: [52.341 ns]

Key Files

  • crates/ordo-core/src/expr/jit/schema_compiler.rs - Schema JIT compiler
  • crates/ordo-core/src/expr/jit/schema_evaluator.rs - JIT evaluator
  • crates/ordo-core/src/expr/jit/typed_context.rs - Typed context
  • crates/ordo-derive/src/lib.rs - TypedContext derive macro
  • crates/ordo-core/benches/ - Benchmarks

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